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Eighty-four post-1990 empirical studies of international tourism demand modeling and forecasting using econometric approaches are reviewed. New developments are identified and it is shown that applications of advanced econometric methods improve the understanding of international tourism demand. An examination of the 22 studies which compare forecasting performance suggests that no single forecasting method can outperform the alternatives in all cases. However, the timevarying parameter (TVP) model and structural time series model with causal variables perform consistently well.

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This study uses meta-analysis to examine the relationship between estimated international tourism demand elasticities and the data characteristics and study features which may affect such empirical estimates. By reviewing 195 studies published during the period 1961-2011, the meta-regression analysis shows that origin, destination, time period, modeling method, data frequency, the inclusion/omission of other explanatory variables and their measures, and sample size all significantly influence the estimates of the demand elasticities generated by a model. Moreover, the demand elasticities at both product and destination levels are generalized by statistically integrating previous empirical estimates. The findings of this meta-analysis will be useful wherever an understanding of the drivers of tourism demand is critically important.